Generative AI's transformative power underscores the critical need for high-quality data. In this session, Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at Fivetran, discuss the nuances of scaling data quality for generative AI applications, highlighting the unique challenges and considerations that come into play. Throughout the session, they share best practices for data and AI leaders to navigate these challenges, ensuring that governance remains a focal point even amid the AI hype cycle. Links Mentioned in the Show: Rewatch Session from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

for a few use cases like static and passive data catalogs. However, active metadata can be the key to unlock a variety of use cases, acting as the glue that binds together our diverse modern data stacks (e.g. dbt, Snowflake, Fivetran, Databricks, Looker, and Tableau) and diverse teams (e.g. analytics engineers, data analysts, data engineers, and business users)! At Atlan, we’ve worked closely with modern data teams like WeWork, Plaid, PayU, SnapCommerce, and Bestow. In this session, we’ll lay out all our learnings about how real-life data teams are using metadata to drive powerful use cases like column-level lineage, programmatic governance, root cause analysis, proactive upstream alerts, dynamic pipeline optimization, cost optimization, data deprecation, automated quality control, metrics management, and more. P.S. We’ll also reveal how active metadata and the dbt Semantic Layer can work together to transform the way your team works with metrics!

Check the slides here: https://docs.google.com/presentation/d/1xrC9yhHOQ00qWt-gVlgbakRELg2FzEPt-RwMsUWzdZA/edit?usp=sharing

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